Conclusion there is information in the category make

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Conclusion: There is information in the category MAKE to be captured, to improve the prediction of Time2repair. ACTION: create and add 3 of the 4 indicators. I will use IBM as the base The regression equation is Time2Repair = 59.2 + 4.66 NUMBER + 0.921 NUMBER^2 + 23.7 Compac + 6.68 Dell - 18.4 MAC Predictor Coef SE Coef T P Constant 59.154 5.764 10.26 0.000 NUMBER 4.6554 0.7450 6.25 0.000 NUMBER^2 0.92096 0.02360 39.03 0.000 Compac 23.650 4.587 5.16 0.000 Dell 6.682 3.857 1.73 0.096 MAC -18.439 5.287 -3.49 0.002 S = 8.04216 R-Sq = 99.9% R-Sq(adj) = 99.9% Analysis of Variance Source DF SS MS F P Regression 5 2599002 519800 8036.95 0.000 Residual Error 24 1552 65 Total 29 2600555 Quick Assessment: The P-value of the F-statistic(0.00) is less than the level of risk, therefore, R 2 is valid.
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On average, 99.8% of the variation in Time2repair can be explained by NUMBER (expressed as a polynomial number + number^2) The p-values of NUMBER (0.005) and NUMBER^2 (0.00) are less than my level or risk, therefore NUMBER (expressed as a polynomial number + number^2) is significantly related to Time2repair. The p-values of the indicators for MAKE (compact=0.00, dell=0.096, Mac = 0.02) are predominantly below my level of risk (0.05) therefore MAKE is significantly related to Time2repair. 1000 800 600 400 200 0 3 2 1 0 -1 -2 -3 Predicted Time2repair in minutes Standardized Residual compac dell ibm mac Make Versus Fits Time2repair vs Number, Number^ 2, and indicators for MAKE Color Plot Assessment: The residuals for compact (black) they are evenly distributed above and below the 0 reference line, they are no longer under predicting Time2repair. The average of the red residuals is about 0.
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